Ever commit code and wonder how it’s faring in the pipeline? Let's make every status update as clear as day. Every time you push new code, a series of automated CI builds and checks spring into action, each telling a part of your project's story. This is where git commit statuses come in—they transform the complex results of these processes into clear, actionable insights. Think of them as real-time signals that indicate whether your code is ready to go live or needs a little more attention.

In this article, you'll learn about N+1 queries, how to detect them with AppSignal, and how to fix them to speed up your Django apps significantly. We'll start with the theoretical aspects and then move on to practical examples. The practical examples will mirror scenarios you might encounter in a production environment. Let's get started!

Releasing a beta version of a Node.js package is a common practice when you want to share a new feature or a breaking change with testers or early adopters without affecting your stable users. Beta releases are tagged with a special version identifier (like 1.0.0-beta.0), making it clear that the version is pre-release and not production-ready. In this guide, we will cover the steps to release a beta version of your package using npm, ensuring a smooth and controlled release process.

Organizations are in a sheer need to stay up-to the minute, and be ahead for protecting their systems and data. Penetration testing can prove to be a structured process that can mimic the real-world vulnerabilities before it’s prone to any malicious exploits. In this entire lifecycle of pentesting, we got some tools in order to automate, streamline, and enhance each phase of it. They could be of various types, based on their specific role in this entire lifecycle.

Democratizing and sharing data inside and outside your organization, as a real-time data stream, has never been more in demand. Treating data as-a-product and adopting Data Mesh practices is leading the way. Here, we explain the concept through a real-life example of an airline building applications that process data across different domains.

The need to democratize and share data inside and outside your organization, as a real-time data stream, has never been more in demand. Treating real-time data as a product, and adopting Data Mesh practices, is the way forward. Here, we explain the concept through a real-life example of an airline building applications that process data across different domains.